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2. Presented by ABHIRAM.S ROLL NO:01 MTECH COMM ENGG Guided by ANOOP CHANDRAN.P ASST.PROF. CAARMEL ENG. COLLEGE 2 3. Introduction Digitalization of topographic maps is an important data source of constructing GIS Maps consist of linear features and backgrounds Linear features are fundamental to GIS ;so its separation is important Manual separation is time consuming Automated separation is based on the colours 3 4. Contd.. 4 When linear feature colour and background colour are similar then it is difficult to separate them This paper present a method based on energy density and shear transform Shear transform preserves lines directional info during one directional separation method Horizontal and vertical templates are used to separate lines from background 5. Contd.. Remaining grid background can be removed by grid template matching Isolated patches of one pixel and less than ten pixels are also removed Union operation on these sheared images give the final result 5 6. Existing systems In 1994 N.Ebi developed a system by converting RGB colour to another colour space In 1994 H.Yan proposed a system based on fuzzy theory ;which combines fuzzy clustering and neural n/ws In 1996 C.Feng developed a system based on colour clustering In 2003 L.Zheng developed a system of fuzzy clustering based on 2D histogram 6 7. Contd.. In 2008 Aria Pezeshek introduced a semi automated method; in this method contour lines are removed by an algorithm based intensity quantization followed by contrast limited adaptive histogram equalization. In 2010 S.Leyk introduced a segmentation method which uses information from local image plane, frequency domain and colour space All methods described above work where the colour difference b/w line and background is seperable 7 8. Characteristic Analysis of Linear Features and Background Colour based separation is difficult in some case 8 9. Figures show histogram of image in lab colour space The are number of peaks in the histogram of first image But in second image colour of pixels are close to each other ; so there is only one peak in the histogram. It is very hard to separate the line from background of second image. 9 10. This figure shows a binary image with complicated background Some portion of the image is ideal and other is complicated 10 1 11. Ideal portion of background can be removed by using the Grid templates shown If the centre pixel and adjacent 8 pixels satisfy the fig 4(a) and 4(a1) then the pixel is treated as background and replaced by 1/white If the centre pixel and adjacent 8 pixels satisfy the fig 4(b) and 4(b1) then the pixel is treated as line info and replaced by 0/black In the fig 3(c) it is a portion of image with complicated background; it cannot be operated with our grid template matching 11 12. Energy characteristics Energy of an image is given by i=1,2,3...M j=1,2,3...N M and N are the height and width of image f(i,j) is the gray value of pixel Energy of one pixel f(i,j) i-kEd1 Ed2>Ed3 ie: energy density of line >energy density of background 19 20. Rule 2: if line and background cannot be separated by rule 1 , it is necessary to control the energy difference of line and background to a certain range Ed=Ed1+Ed2+Ed3/3 T=Ed2-Ed+ is acquired by experience; =3000-5000 Ed2-Ed1>T Ed2-Ed3>T h2 is treated as line if and only if Ed2 satisfies rule 1 and rule 2 20 21. Background pixel h1 and h3 and isolated patches of one pixel or less than ten pixels are removed Finally union operation is performed on the two images 21 22. Shear transform Shear transform is a linear transform that displaces point in a fixed direction Introduced to avoid the separation difficulties while operating lines with many direction Ws,k is the shear operation s=0,1 k[-2ndir ,2ndir] fs,k(x,y)=f(x,y)*Ws,k Total number of sheared image is given by 2ndir+1+1 22 23. Shear transform is performed by sampling pixel according to the shear matrix S=0 operation is performed in horizontal direction S=1 operation is performed in vertical direction (x,y)=(x,y)S1=(x,y) 23 24. This is the result of shear transform of s=0, ndir=2 so a total of 9 images; union of these images gives a perfect map 24 25. Steps of proposed method STEP 1: colour image is converted into gray image Gray=0.233R+0.587G+0.114B negative of the gray image is taken I=e*255-gray e is a matrix with same size of gray matrix with all elements equal to one STEP 2: Apply shear transform to the negative image 25 26. Contd.. STEP 3: Establishment of template: horizontal and vertical STEP 4: Linear feature separation from background: i.e. : energy of each area in template is calculated, line is separated from background by rule 1 and rule 2 with =4000 26 27. Contd... STEP 5: Removal of miscellaneous point: i.e. : remaining grid background can be removed by grid template matching and isolated points can also be removed STEP 6: Inverse shear transform and union operation 27 28. 28 29. Experiments and Discussions This is a 7 colour topographical map image of size 342*198 size Colour of linear feature and background are similar here so it is very difficult to separate lines from background 29 30. Here size of h2 is 2*2 h1&h3 is 4*2 if vertical template is used h1&h3 is 2*4 if horizontal template is used =4000 Fig(b) is the gray image Fig(c) is the negative image 30 31. The first set of figures shows the sheared images with k=-1, k=0, k=1 Second set shows energy density based extraction by templates 31 32. Fig (a) shows the union operation of a2, b2, c2, Fig (b) shows lines with colour info extracted from colour image Fig (c) shows the remaining background 32 33. Comparison 33 34. 34 35. Conclusion This paper proposes a method to linear separation from background Here shear transform is used to overcome the limitation of directions for lines Energy density concept is introduced to separate lines from background The new method can easily be applied to maps for efficient separation of lines Adaptive size fixing of template is a draw back of this method 35 36. Reference R. Samet and E. Hancer, A new approach to the reconstruction of contour lines extracted from topographic maps, J. Visual Commun. E. Hancer and R. Samet, Advanced contour reconnection in scanned topographic maps, H. Chen, X.-A. Tang, C.-H. Wang, and Z. Gan, Object oriented segmentation of scanned topographical maps, S. Leyk, Segmentation of colour layers in historical maps based on hierarchical colour sampling, in Graphics Recognition. Achievements, Challenges, and Evolution (Lecture Notes in Computer Science), 36 37. Thank you 37 38. Questions? 38